Business Improvement Areas and the Role of the Visitor Economy in the Survival, Recovery, and Progression of Main Streets in the Context of COVID-19
Bibliographic record
Abstract
Business Improvement Areas (BIAs) are associations of property owners and tenants within a defined area who undertake initiatives to create successful business environments and attract people to them. This fundamental function shares similarities with the idea of a tourism destination, which manages the quality of the destination and attracts visitors to it. Despite this natural connectivity, current literature has yet to explore the relationship. BIAs may benefit from the application of a destination mindset to the BIA model to strategically attract and engage visitors, particularly as a long-term COVID-19 recovery strategy. This exploratory study involved a thematic analysis of 32 semi-structured interviews with Canadian BIA representatives. Findings demonstrate that BIAs are important actors in local visitor economy development and engagement, and that the visitor economy is important for BIAs in achieving their objectives. This thesis offers 13 recommendations for BIAs to consider to more effectively engage the visitor economy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".